Dr Jose Romeo (Pepe), PhD


Dr Jose Romeo (Pepe), is a researcher in Statistics, specialised in multivariate survival analysis and interested in applying statistical models for decision making in public health and social sciences. He holds a Statistical Engineering degree from the University of Santiago, Chile, and a PhD degree in Statistics from the University of Sao Paulo, Brazil.

Since December 2016, He has been working at SHORE & Whariki Research Centre providing statistical expertise on quantitative research projects led by social scientists and public health researchers on topics including alcohol consumption, alcohol policy, drugs trends and Maori attitudes towards alcohol. His role includes providing advice on research design and analyses as grant applications are developed, data management and statistical analyses on current research and collaborating on the dissemination of findings through monographs and peer reviewed publications.

Research Interests:
Statistical Modelling | Biostatistics | Bayesian Inference | Regression models | Survival Analysis | Copula and Frailty models | Multivariate methods


journal articles

Huckle, T., Romeo, J., Wall, M., Callinan, S., Holmes, J., Meier, P., Mackintosh, A.-M., Piazza, M., Chaiyasong, S., Cuong, P. V., Casswell, S. (2018). Socio-economic disadvantage is associated with heavier drinking in high but not middle-income countries participating in the International Alcohol Control (IAC) Study. Drug and Alcohol Review, published online Apr 30. A437

Romeo, J.S., Meyer, R. and Gallardo, D.I. (2018). Bayesian bivariate survival analysis using the power variance function copula. Lifetime Data Analysis, 24, 355-383.

Gallardo, D.I., Romeo, J.S. and Meyer, R. (2017). A simplified estimation procedure based on the EM algorithm for the power series cure rate model. Communications in Statistics - Simulation and Computation, 46(8), 6342-6359.

Poshdar, M., Gonzalez, V.A., Raftery, G.M., Orozco, F., Romeo, J.S. and Forcael, E. (2016). A probabilistic-based method to determine optimum size of project buffer in construction schedules. Journal of Construction Engineering and Management, 142(10), 04016046.

Meyer, R. and Romeo, J.S. (2015). Bayesian semiparametric analysis of recurrent failure time data using copulas. Biometrical Journal, 57, 982-1001.

Romeo, J.S. and Meyer, R. (2015). Bayesian approach for modelling bivariate survival data through the PVF copula. In Friedl, H. and Wagner, H. (Eds.), Proceedings of the 30th International Workshop on Statistical Modelling, vol. 2. Linz, Austria, 239-242.

Reyes-Lopez, F.E., Romeo, J.S., Vallejos-Vidal, E., Reyes-Cerpa, S., Sandino, A.M., Tort, L., Mackenzie, S. and Imarai, M. (2015). Differential immune gene expression profiles in susceptible and resistant full-sibling families of Atlantic salmon (Salmo salar) challenged with infectious pancreatic necrosis virus (IPNV). Developmental & Comparative Immunology, 53, 210-221.

Romeo, J.S., Meyer, R. and Reyes-Lopez, F. (2014). Hierarchical failure time regression using mixtures for classification of the immune response of Atlantic salmon. Journal of Agricultural, Biological, and Environmental Statistics, 19(4), 501-521.

Roman, S.T., Romeo, J.S. and Salinas, V.H. (2014). Bayesian estimation of the limiting availability in the presence of right-censored data. METRON, 72, 247-267.

Bazan, J.L., Romeo, J.S. and Rodrigues, J. (2014). Bayesian skew-probit regression for binary response data. Brazilian Journal of Probability and Statistics, 28, 467-482.

Torres-Aviles, F., Romeo, J.S. and Lopez-Kleine, L. (2014). Data mining and influential analysis of gene expression data for plant resistance genes identification in tomato (Solanum lycopersicum). Electronic Journal of Biotechnology, 17, 79-82.

Lopez-Kleine, L., Romeo, J.S. and Torres-Aviles, F. (2013). Gene functional prediction using clustering methods for the analysis of tomato microarray data. In Mohamad, M.S., Nanni, L., Rocha, M.P. and Fdez-Riverola, F. (Eds.), 7th International Conference on Practical Applications of Computational Biology & Bioinformatics, Advances in Intelligent Systems and Computing, vol. 222. Springer International Publishing, Switzerland, 1-6.

Romeo, J.S., Torres-Aviles, F. and Lopez-Kleine, L. (2013). Detection of influent virulence and resistance genes in microarray data through quasi likelihood modeling. Molecular Genetics and Genomics, 288, 49-61.

Romeo, J.S., Tanaka, N.I., Pedroso-de-Lima, A.C. and Salinas-Torres, V.H. (2013). Large sample properties for a class of copulas in bivariate survival analysis. Metrika, 76, 997-1015.

Salinas, V.H., Romeo, J.S. and Pena, J.A. (2010). On Bayesian estimation of a survival curve: comparative study and examples. Computational Statistics, 25, 375-389.

Diaz-Ledezma, C., Urrutia, J., Romeo, J.S., Chelen, A., Gonzalez-Wilhelm, L. and Lavarello, C. (2009). Factors associated with variability in length of sick leave because of acute low back pain in Chile. The Spine Journal, 9, 1010-1015.

Romeo, J.S., Tanaka, N.I. and Pedroso-de-Lima, A.C. (2006). Bivariate survival modeling: A Bayesian approach based on copulas. Lifetime Data Analysis, 12, 205-222.


Technical Reports

Huckle, T. and Romeo, P. (2018). Patterns of social supply of alcohol over time in New Zealand. Health Promotion Agency, Wellington, New Zealand. ISBN: 978-0-478-44926-6

Wilkins, C., Prasad, J., Romeo, J.S. and Rychert, M. (2017). Recent trends in illegal drug use in New Zealand, 2006-2016: Findings from the Illicit Drug Monitoring System (IDMS). SHORE, College of Health, Massey University, Auckland, New Zealand. ISBN: 1-877428-28-0

Wilkins, C., Prasad, J., Moewaka Barnes, H., Romeo, J.S. and Rychert, M. (2017). New Zealand Arrestee Drug Use Monitoring (NZ-ADUM) 2016 Report. SHORE, College of Health, Massey University, Auckland, New Zealand.